A multi-objective simulation-based optimization approach for inventory replenishment problem with premium freights in convergent supply chains

Mualla Gonca Avci and Hasan Selim

Omega, 2018, vol. 80, issue C, 153-165

Abstract: In this study, a multi-objective simulation-based optimization approach is developed to solve inventory replenishment problem with premium freights in convergent supply chains. In this context, a decomposition-based multi-objective differential evolution algorithm (MODE/D) is used to determine demand forecast adjustment factor, safety stock and supplier flexibility parameters that minimize total holding cost, inbound and outbound premium freight ratios simultaneously. The proposed approach is applied a set of problem instances and the performance of the proposed approach is evaluated in comparison with the performance of non-dominated sorting genetic algorithm-II (NSGA-II). Furthermore, the proposed approach is applied to a multi-national automotive supply chain spread on Europe. The results reveal that the proposed approach is effective in solving inventory replenishment problem with premium freights in convergent supply chains.

Keywords: Inventory replenishment; Supply chain risk; Premium freight; Multi-objective optimization; Simulation-based optimization; Differential evolution (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (9)

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DOI: 10.1016/j.omega.2017.08.016

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